Package ‘aLFQ’ March 23, 2017 Type Package Title Estimating Absolute Protein Quantities from Label-Free LC-MS/MS Proteomics Data Version 1.3.4 Date 2017-03-23 Author George Rosenberger, Hannes Roest, Christina Ludwig, Ruedi Aebersold, Lars Malmstroem Maintainer George Rosenberger <[email protected]> Depends R (>= 2.15.0) Imports data.table, plyr, caret, seqinr, lattice, randomForest, ROCR, reshape2, protiq, bio3d Suggests testthat Description Determination of absolute protein quantities is necessary for multiple applications, such as mech- anistic modeling of biological systems. Quantitative liquid chromatography tandem mass spec- trometry (LC-MS/MS) proteomics can measure relative protein abundance on a system- wide scale. To estimate absolute quantitative information using these relative abundance mea- surements requires additional information such as heavy-labeled references of known concentra- tion. Multiple methods have been using different references and strategies; some are easily avail- able whereas others require more effort on the users end. Hence, we believe the field might bene- fit from making some of these methods available under an automated framework, which also fa- cilitates validation of the chosen strategy. We have implemented the most commonly used abso- lute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on ei- ther MS1-, MS2-levels or spectral counts together with validation algorithms to enable auto- mated data analysis and error estimation. Specifically, we used Monte-carlo cross- validation and bootstrapping for model selection and imputation of proteome-wide absolute pro- tein quantity estimation. Our open-source software is written in the statistical programming lan- guage R and validated and demonstrated on a synthetic sample. License GPL (>= 3) URL https://github.com/aLFQ NeedsCompilation no Repository CRAN Date/Publication 2017-03-23 15:28:19 UTC 1
Transcript
Package ‘aLFQ’March 23, 2017
Type Package
Title Estimating Absolute Protein Quantities from Label-Free LC-MS/MSProteomics Data
Version 1.3.4
Date 2017-03-23
Author George Rosenberger, Hannes Roest, Christina Ludwig, Ruedi Aebersold, Lars Malmstroem
DescriptionDetermination of absolute protein quantities is necessary for multiple applications, such as mech-anistic modeling of biological systems. Quantitative liquid chromatography tandem mass spec-trometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance mea-surements requires additional information such as heavy-labeled references of known concentra-tion. Multiple methods have been using different references and strategies; some are easily avail-able whereas others require more effort on the users end. Hence, we believe the field might bene-fit from making some of these methods available under an automated framework, which also fa-cilitates validation of the chosen strategy. We have implemented the most commonly used abso-lute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on ei-ther MS1-, MS2-levels or spectral counts together with validation algorithms to enable auto-mated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute pro-tein quantity estimation. Our open-source software is written in the statistical programming lan-guage R and validated and demonstrated on a synthetic sample.
Estimating Absolute Protein Quantities from Label-Free LC-MS/MS Proteomics Data
Details
Package: aLFQType: PackageVersion: 1.3.4Date: 2017-03-23Author: George Rosenberger, Hannes Roest, Christina Ludwig, Ruedi Aebersold, Lars MalmstroemMaintainer: George Rosenberger <[email protected]>Depends: R (>= 2.15.0)Imports: data.table, plyr, caret, seqinr, lattice, randomForest, ROCR, reshape2, protiq, bio3dSuggests: testthatLicense: GPL version 3 or newerURL: https://github.com/aLFQNeedsCompilation: noRepository: CRAN
Determination of absolute protein quantities is necessary for multiple applications, such as mecha-nistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrom-etry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. Toestimate absolute quantitative information using these relative abundance measurements requiresadditional information such as heavy-labeled references of known concentration. Multiple methodshave been using different references and strategies; some are easily available whereas others require
more effort on the users end. Hence, we believe the field might benefit from making some of thesemethods available under an automated framework, which also facilitates validation of the chosenstrategy. We have implemented the most commonly used absolute label-free protein abundance es-timation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral countstogether with validation algorithms to enable automated data analysis and error estimation. Specif-ically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputationof proteome-wide absolute protein quantity estimation. Our open-source software is written in thestatistical programming language R and validated and demonstrated on a synthetic sample.
Absolute label-free quantification of mass spectrometry proteomics ex-periments
Description
Absolute label-free quantification of mass spectrometry proteomics experiments.
Usage
## Default S3 method:AbsoluteQuantification(data, total_protein_concentration = 1, ...)## S3 method for class 'AbsoluteQuantification'cval(object, cval_method = "mc", mcx = 1000, ...)## S3 method for class 'AbsoluteQuantification'print(x, ...)## S3 method for class 'AbsoluteQuantification'plot(x, ...)## S3 method for class 'AbsoluteQuantification'hist(x, ...)## S3 method for class 'AbsoluteQuantification'pivot(x, ...)## S3 method for class 'AbsoluteQuantification'export(x, file, ...)
4 AbsoluteQuantification
Arguments
data a mandatory data frame containing the columns "run_id", "protein_id", "response",and "concentration" as generated by ProteinInference. The id column can bedefined in any format, while the "response" and "concentration" columnsneed to be numeric and in non-log form. The data may contain calibration data(with numeric "concentration" and test data (with "concentration" = "?"))
total_protein_concentration
the total protein concentration in the sample in any unit. This will be used forthe normalized protein and concentration columns.
object an AbsoluteQuantification object.
cval_method a method for doing crossvalidation: "boot" (bootstrapping), "mc" (monte carlocross-validation), "loo" (leaving-one-out).
mcx a positive integer value of the number of folds for cross-validation.
file the location of the output csv file.
x an AbsoluteQuantification object.
... future extensions.
Details
If absolute quantity estimation based on anchor peptides or proteins is demanded, the calibrationpeptide or protein abundance must be provided. Both estimated calibration protein intensities andseparately determined calibration protein concentrations are log transformed and a first order linearleast-squares regression of this log-log data is calculated. The abundance of the target proteins ispredicted based on this regression. The error of the regression arises from biological and technicalvariation as well from the protein and peptide intensity estimators. To perform model selection andto estimate the error of the predicted protein concentrations, bootstrapping and Monte Carlo cross-validation as suggested (Malmstrom et al., 2009; Ludwig et al., 2012) were implemented. For bothmethods, the objective function is the minimization of the mean fold-error.
If, on the other hand, the total protein concentration per cell is supplied in proteome-wide experi-ments, the absolute protein concentrations are estimated by normalization of the MS intensities orspectral counts to this number (Lu et al., 2006).
Malmstrom, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Lep-tospira interrogans. Nature 460, 762-765 (2009).
Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of Absolute Protein Quantitiesof Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular & CellularProteomics 11, M111.013987-M111.013987 (2012).
ALF 5
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling es-timates the relative contributions of transcriptional and translational regulation. Nat Biotech 25,117-124 (2006).
UPS2_SRM<-head(UPS2_SRM,100) # Remove this line for real applicationsdata_PI <- ProteinInference(UPS2_SRM)data_AQ <- predict(cval(AbsoluteQuantification(data_PI),mcx=2))print(data_AQ)plot(data_AQ)hist(data_AQ)pivot(data_AQ)
ALF Generate ALF report
Description
Estimation of Absolute Protein Quantities of Unlabeled Samples by Targeted Mass Spectrometry.
data a mandatory data frame containing the columns "run_id", "protein_id", "peptide_id","peptide_sequence", "precursor_charge", "peptide_intensity" and "concentration"are required. For quantification on the transition level, the columns "protein_id","peptide_id", "transition_id", "peptide_sequence", "precursor_charge","transition_intensity" and "concentration" are required. The id columns
6 ALF
can be defined in any format, while the "_intensity" and "concentration"columns need to be numeric and in non-log form. The data may contain calibra-tion data (with numeric "concentration" and test data (with "concentration"= "?"))
report_filename
the path and filename of the PDF report.prediction_filename
the path and filename of the predictions of the optimal model.peptide_methods
a vecter containing a combination of "top", "all", "iBAQ", "APEX", "NSAF" orcode"SCAMPI" peptide to protein intensity estimation methods.
peptide_topx ("top" only:) a positive integer value of the top x peptides to consider for "top"methods.
peptide_strictness
("top" only:) whether peptide_topx should only consider proteins with theminimal peptide number ("strict") or all ("loose").
peptide_summary
("top" and "all" only:) how to summarize the peptide intensities: "mean","median", "sum".
transition_topx
a positive integer value of the top x transitions to consider for transition to pep-tide intensity estimation methods.
transition_strictness
whether transition_topx should only consider peptides with the minimal tran-sition number ("strict") or all ("loose").
transition_summary
how to summarize the transition intensities: "mean", "median", "sum".fasta ("iBAQ", "APEX", "NSAF" and "SCAMPI" only:) the path and filename to an
amino acid fasta file containing the proteins of interest.apex_model ("APEX" only:) The "APEX" model to use (see APEX).combine_precursors
whether to sum all precursors of the same peptide.combine_peptide_sequences
whether to sum all variants (modifications) of the same peptide sequence.consensus_proteins
if multiple runs are provided, select identical proteins among all runs.consensus_peptides
if multiple runs are provided, select identical peptides among all runs.consensus_transitions
if multiple runs are provided, select identical transitions among all runs.scampi_method (SCAMPI only:) Describes which method should be used for the parameter es-
timation. Available: method="LSE", method="MLE". See details of runScampior iterateScampi.
scampi_iterations
(SCAMPI only:) Only used with scampi_method="MLE". See details of run-Scampi or iterateScampi.
ALF 7
scampi_outliers
(SCAMPI only:) Whether runScampi (FALSE) or iterateScampi (TRUE) shouldbe used. See details of runScampi or iterateScampi.
scampi_outliers_iterations
(SCAMPI only:) Number of estimation/outlier-removal iterations to be per-formed. See details of iterateScampi.
scampi_outliers_threshold
(SCAMPI only:) Constant to tune the outlier selection process. See details ofiterateScampi.
cval_method a method for doing crossvalidation: "boot" (bootstrapping), "mc" (monte carlocross-validation), "loo" (leaving-one-out).
cval_mcx a positive integer value of the number of folds for cross-validation.
... future extensions.
Details
The ALF module enables model selection for TopN transitions and peptides for protein quantifi-cation (Ludwig et al., 2012). The workflow is completely automated and a report and prediction(using the best model) is generated.
Ludwig, C., Claassen, M., Schmidt, A. \& Aebersold, R. Estimation of Absolute Protein Quantitiesof Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular \& CellularProteomics 11, M111.013987-M111.013987 (2012).
APEX Training, testing and validation of APEX peptide observability models
Description
Calculating absolute and relative protein abundance from mass spectrometry-based protein expres-sion data.
Usage
## Default S3 method:APEX(data, ...)## S3 method for class 'APEX'predict(object, newdata=NULL, ...)## S3 method for class 'APEX'cval(object, folds=10, ...)## S3 method for class 'APEX'print(x, ...)## S3 method for class 'APEX'plot(x, ...)
Arguments
data an R object of type "apexFeatures".
object an APEX object.
newdata an R object of type "apexFeatures".
folds a positive integer value of the number of folds for cross-validation.
x an APEX object.
... future extensions.
Details
The APEX module is a reimplementation of the original algorithm (Lu et al., 2006; Vogel et al.,2008) using the randomForest package. It requires apexFeatures input objects and reports theresults in an APEX object, which can be used by the ProteinInference module for protein quan-tification.
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling es-timates the relative contributions of transcriptional and translational regulation. Nat Biotech 25,117-124 (2006).
Vogel, C. & Marcotte, E. M. Calculating absolute and relative protein abundance from massspectrometry-based protein expression data. Nat Protoc 3, 1444-1451 (2008).
APEX_ORBI<-head(APEX_ORBI,50) # Remove this line for real applicationsAPEX_ORBI.af <- apexFeatures(APEX_ORBI)APEX_ORBI.apex <- APEX(data=APEX_ORBI.af)print(APEX_ORBI.apex)
apexFeatures Calculation of physicochemical amino acid properties for APEX
Description
Calculation of physicochemical amino acid properties for APEX.
Usage
## Default S3 method:apexFeatures(x, ...)## S3 method for class 'apexFeatures'print(x, ...)
Arguments
x a mandatory data frame containing the variables in the model. The data framerequires the columns "peptide_sequence", "apex". The data may containtraining data (with boolean "apex" and test data (with "apex"=NA))
... future extensions.
10 APEXMS
Details
The apexFeatures function computes the APEX or PeptideSieve features (Mallick et al., 2006; Vogelet al., 2008) based on AAindex (Kawashima et al., 2008) and returns them in an apexFeatures objectfor further usage in the APEX module.
Kawashima, S. et al. AAindex: amino acid index database, progress report 2008. Nucleic AcidsResearch 36, D202-5 (2008).
Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. NatBiotech 25, 125-131 (2006).
Vogel, C. & Marcotte, E. M. Calculating absolute and relative protein abundance from massspectrometry-based protein expression data. Nat Protoc 3, 1444-1451 (2008).
# APEX_ORBIAPEX_ORBI<-head(APEX_ORBI,20) # Remove this line for real applicationsAPEX_ORBI.af <- apexFeatures(APEX_ORBI)print(APEX_ORBI.af)
# APEX_LCQAPEX_LCQ<-head(APEX_LCQ,20) # Remove this line for real applicationsAPEX_LCQ.af <- apexFeatures(APEX_LCQ)print(APEX_LCQ.af)
APEXMS Calculating absolute and relative protein abundance from massspectrometry-based protein expression data.
Description
This dataset contains training ThermoFinnigan LTQ-OrbiTrap (ORBI) and ThermoFinnigan Sur-veyor/DecaXP+ iontrap (LCQ) data for training of an APEX classifier. The dataset was generated byChristine Vogel and Edward M. Marcotte (see references).
import 11
Usage
data(APEXMS)
Format
A data.frame with the following components:
1. peptide_sequence character vector: Peptide sequence.
2. apex character vector: observed in experiment: 0 = no; 1 = yes.
Source
The dataset was obtained from:http://marcottelab.org/APEX_Protocol/.
References
Vogel, C. & Marcotte, E. M. Calculating absolute and relative protein abundance from massspectrometry-based protein expression data. Nat Protoc 3, 1444-1451 (2008).
ms_filenames the paths and filenames of files to import in a character or array class.
ms_filetype one of "openswath", "mprophet", "openmslfq", "skyline", "abacus" or"pepxml2csv" filetypes. Multiple files of the same type can be supplied in avector.
concentration_filename
the filename of a csv with concentrations (in any unit). Needs to have thecolumns "peptide_id" (or "protein_id") and "concentration".
averageruns whether different MS runs should be averaged.
sumruns whether different MS runs should be summed.mprophet_cutoff
(openswath and mprophet only:) the FDR cutoff (m_score) for OpenSWATHand mProphet reports.
openswath_superimpose_identifications
(openswath only:) enables propagation of identification among several runs iffeature alignment or requantification was conducted.
openswath_replace_run_id
whether the run_id of the MS data should be replaced by the filename.openswath_filtertop
(openswath only:) whether only the top peakgroup should be considered.openswath_removedecoys
(openswath only:) whether decoys should be removed.peptideprophet_cutoff
(abacus and pepxml2csv only:) the PeptideProphet probability cutoff for Abacusreports.
abacus_column (abacus only:) target score: one of "NUMSPECSTOT","TOTNSAF","NUMSPECSUNIQ","UNIQNSAF","NUMSPECSADJ" or "ADJNSAF".
pepxml2csv_runsplit
(pepxml2csv only:) the separator of the run_id and spectrum_id column.
... future extensions.
Details
The import function provides unified access to the results of various standard proteomic quantifi-cation applications like OpenSWATH (Roest et al., 2014), mProphet (Reiter et al., 2011), OpenMS(Sturm et al., 2008; Weisser et al., 2013),Skyline (MacLean et al., 2010) and Abacus (Fermin et al.,2011). This enables generic application of all further steps using the same data structure and enablesextension to support other data formats. If multiple runs, i.e. replicates, are supplied, the averagedor summed values can be used to summarize the experimental data. In addition to the input from theanalysis software, an input table with the anchor peptides or proteins and sample specific absoluteabundance, or an estimate of the total protein concentration in the sample is required. The endpointof this step is a unified input data structure.
Value
A standard aLFQ import data frame, either on transition, peptide (precursor) or protein level.
Roest H. L. et al. A tool for the automated, targeted analysis of data-independent acquisition (DIA)MS-data: OpenSWATH. Nat Biotech, in press.
Reiter, L. et al. mProphet: automated data processing and statistical validation for large-scaleSRM experiments. Nat Meth 8, 430-435 (2011).
Sturm, M. et al. OpenMS - An open-source software framework for mass spectrometry. BMCBioinformatics 9, 163 (2008).
Weisser, H. et al. An automated pipeline for high-throughput label-free quantitative proteomics. J.Proteome Res. 130208071745007 (2013). doi:10.1021/pr300992u
MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targetedproteomics experiments. Bioinformatics 26, 966-968 (2010).
Fermin, D., Basrur, V., Yocum, A. K. & Nesvizhskii, A. I. Abacus: A computational tool forextracting and pre-processing spectral count data for label-free quantitative proteomic analysis.PROTEOMICS 11, 1340-1345 (2011).
LUDWIGMS Estimation of Absolute Protein Quantities of Unlabeled Samples bySelected Reaction Monitoring Mass Spectrometry..
Description
This dataset contains the Leptospira interrogans MS data from Ludwig C., et al. (2012).
Usage
data(LUDWIGMS)
Format
The data structure for LUDWIG_SRM represents a data.frame containing the following columnheader: "run_id" (freetext), "protein_id" (freetext), "peptide_id" (freetext), "transition_id"(freetext), "peptide_sequence" (unmodified, natural amino acid sequence in 1-letter nomencla-ture), "precursor_charge" (positive integer value), "transition_intensity" (positive non-logarithm floating value) and "concentration" (calibration: positive non-logarithm floating value,prediction: "?").
References
Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of Absolute Protein Quantitiesof Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular & CellularProteomics 11, M111.013987-M111.013987 (2012).
data a mandatory data frame containing the "protein_id", "peptide_id", "transition_id","peptide_sequence", "precursor_charge", "transition_intensity" and"concentration" are required. The id columns can be defined in any format,while the "_intensity" and "concentration" columns need to be numericand in non-log form. The data may contain calibration data (with numeric"concentration" and test data (with "concentration" = "?"))
transition_topx
a positive integer value of the top x transitions to consider for transition to pep-tide intensity estimation methods.
transition_strictness
whether transition_topx should only consider peptides with the minimal tran-sition number ("strict") or all ("loose").
transition_summary
how to summarize the transition intensities: "mean", "median", "sum".consensus_proteins
if multiple runs are provided, select identical proteins among all runs.consensus_transitions
if multiple runs are provided, select identical transitions among all runs.
... future extensions.
Details
The PeptideInference module provides functionality to infer peptide / precursor quantities from themeasured precursor or fragment intensities or peptide spectral counts.
Value
A standard aLFQ import data frame on peptide / precursor level.
Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of Absolute Protein Quantitiesof Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular & CellularProteomics 11, M111.013987-M111.013987 (2012).
data a mandatory data frame containing the columns "run_id", "protein_id", "protein_intensity",and "concentration" for quantification on the protein level. For quantificationon the peptide level, the columns "run_id", "protein_id", "peptide_id","peptide_sequence", "precursor_charge", "peptide_intensity" and "concentration"are required. For quantification on the transition level, the columns "protein_id","peptide_id", "transition_id", "peptide_sequence", "precursor_charge","transition_intensity" and "concentration" are required. The id columns
ProteinInference 17
can be defined in any format, while the "_intensity" and "concentration"columns need to be numeric and in non-log form. The data may contain calibra-tion data (with numeric "concentration" and test data (with "concentration"= "?"))
peptide_method one of "top", "all", "iBAQ", "APEX", "NSAF" or "SCAMPI" peptide to proteinintensity estimation methods.
peptide_topx ("top" only:) a positive integer value of the top x peptides to consider for "top"methods.
peptide_strictness
("top" only:) whether peptide_topx should only consider proteins with theminimal peptide number ("strict") or all ("loose").
peptide_summary
("top" and "all" only:) how to summarize the peptide intensities: "mean","median", "sum".
transition_topx
a positive integer value of the top x transitions to consider for transition to pep-tide intensity estimation methods.
transition_strictness
whether transition_topx should only consider peptides with the minimal tran-sition number ("strict") or all ("loose").
transition_summary
how to summarize the transition intensities: "mean", "median", "sum".
fasta ("iBAQ", "APEX", "NSAF" and "SCAMPI" only:) the path and filename to anamino acid fasta file containing the proteins of interest.
apex_model ("APEX" only:) The "APEX" model to use (see APEX).combine_precursors
whether to sum all precursors of the same peptide.combine_peptide_sequences
whether to sum all variants (modifications) of the same peptide sequence.consensus_proteins
if multiple runs are provided, select identical proteins among all runs.consensus_peptides
if multiple runs are provided, select identical peptides among all runs.consensus_transitions
if multiple runs are provided, select identical transitions among all runs.
scampi_method (SCAMPI only:) Describes which method should be used for the parameter es-timation. Available: method="LSE", method="MLE". See details of runScampior iterateScampi.
scampi_iterations
(SCAMPI only:) Only used with scampi_method="MLE". See details of run-Scampi or iterateScampi.
scampi_outliers
(SCAMPI only:) Whether runScampi (FALSE) or iterateScampi (TRUE) shouldbe used. See details of runScampi or iterateScampi.
18 ProteinInference
scampi_outliers_iterations
(SCAMPI only:) Number of estimation/outlier-removal iterations to be per-formed. See details of iterateScampi.
scampi_outliers_threshold
(SCAMPI only:) Constant to tune the outlier selection process. See details ofiterateScampi.
... future extensions.
Details
The ProteinInference module provides functionality to infer protein quantities from the measuredprecursor or fragment intensities or peptide spectral counts. If the dataset contains targeted MS2-level data, the paired precursor and fragment ion signals, the transitions, are first summarized tothe precursor level. Different methods for aggregation can be specified, e.g. sum, mean or medianand a limit for the selection of the most intense transitions can be provided. It is further possible toexclude precursors, which do not have sufficient transitions to fulfill this boundary. To summarizeprecursor intensities or spectral counts to theoretical protein intensities, the mean, TopN (Silva etal., 2006; Malmstrom et al., 2009; Schmidt et al., 2011; Ludwig et al., 2012), APEX (Lu et al.,2006), iBAQ (Schwanhausser et al., 2011), NSAF (Zybailov et al., 2006) and SCAMPI (Gersteret al., 2014) protein intensity estimators are provided. For APEX, iBAQ, NSAF and SCAMPI, theprotein database in FASTA format needs to be supplied. In terms of targeted data acquisition, forboth APEX and iBAQ methods all peptides of a protein must be targeted. The results are reportedin the same unified data structure as from the previous step
Value
A standard aLFQ import data frame on protein level.
Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, Johannes P. C. & Geromanos, S. J. Absolutequantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell Proteomics 5,144-156 (2006).
Malmstrom, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Lep-tospira interrogans. Nature 460, 762-765 (2009).
Schmidt, A. et al. Absolute quantification of microbial proteomes at different states by directedmass spectrometry. Molecular Systems Biology 7, 1-16 (2011).
Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of Absolute Protein Quantitiesof Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular & CellularProteomics 11, M111.013987-M111.013987 (2012).
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling es-timates the relative contributions of transcriptional and translational regulation. Nat Biotech 25,117-124 (2006).
proteotypic 19
Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473,337-342 (2011).
Zybailov, B. et al. Statistical Analysis of Membrane Proteome Expression Changes in Saccha-romyces c erevisiae. J. Proteome Res. 5, 2339-2347 (2006).
Gerster S., Kwon T., Ludwig C., Matondo M., Vogel C., Marcotte E. M., Aebersold R., BuhlmannP. Statistical approach to protein quantification. Molecular & Cellular Proteomics 13, M112.02445(2014).
min_aa the minimum number of amino acids for proteotypic peptides.
max_aa the maximum number of amino acids for proteotypic peptides.
... future extensions.
Details
This function provides prediction of the "flyability" of proteotypic peptides using the APEX method(Lu et al., 2006; Vogel et al., 2008). The APEX scores are probabilities that indicate detectabilityof the peptide amino acid sequence in LC-MS/MS experiments.
20 UPS2MS
Value
A data.frame containing peptide sequences and associated APEX scores.
Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E. M. Absolute protein expression profiling es-timates the relative contributions of transcriptional and translational regulation. Nat Biotech 25,117-124 (2006).
Vogel, C. & Marcotte, E. M. Calculating absolute and relative protein abundance from massspectrometry-based protein expression data. Nat Protoc 3, 1444-1451 (2008).
APEX_ORBI<-head(APEX_ORBI,20) # Remove this line for real applicationsAPEX_ORBI.af <- apexFeatures(APEX_ORBI)APEX_ORBI.apex <- APEX(data=APEX_ORBI.af)
peptides <- proteotypic(fasta=system.file("extdata","example.fasta",package="aLFQ"),apex_model=APEX_ORBI.apex, min_aa=4 , max_aa=20)## Not run: print(peptides)
UPS2MS Calculating absolute and relative protein abundance from massspectrometry-based protein expression data.
Description
We assessed the performance of aLFQ and the different quantification estimation methods it sup-ports by investigating a commercially available synthetic sample. The Universal Proteomic Stan-dard 2 (UPS2) consists of 48 proteins spanning a dynamic range of five orders of magnitude inbins of eight proteins. The sample was measured in a complex background consisting of Mycobac-terium bovis BCG total cell lysate in shotgun and targeted MS modes. Three datasets are available:UPS2_SC (spectral counts), UPS2_LFQ (MS1 intensity), UPS2_SRM (MS2 intensity).
UPS2MS 21
Usage
data(UPS2MS)
Format
The data structure for UPS2_SRM represents a data.frame containing the following column header:"run_id" (freetext), "protein_id" (freetext), "peptide_id" (freetext), "transition_id" (free-text), "peptide_sequence" (unmodified, natural amino acid sequence in 1-letter nomenclature),"precursor_charge" (positive integer value), "transition_intensity" (positive non-logarithmfloating value) and "concentration" (calibration: positive non-logarithm floating value, predic-tion: "?").
The data structure for UPS2_LFQ (MS1-level intensity) / UPS2_SC (spectral counts) represents adata.frame containing the columns "run_id" (freetext), "protein_id" (freetext), "peptide_id"(freetext), "peptide_sequence" (unmodified, natural amino acid sequence in 1-letter nomencla-ture), "precursor_charge" (positive integer value), "peptide_intensity" (positive non-logarithmfloating value) and "concentration" (calibration: positive non-logarithm floating value, predic-tion: "?"). It should be noted, that the spectral count value is also represented by "peptide_intensity".
